結構練習 的英文怎麼說

中文拼音 [jiēgòuliànxí]
結構練習 英文
structure practice
  • 結 : 結動詞（長出果實或種子） bear （fruit）; form （seed）
  • 構 : Ⅰ動詞1 （構造; 組合） construct; form; compose 2 （結成） fabricate; make up 3 （建造; 架屋） bui...
  • 練 : Ⅰ名詞1 （白絹） white silk 2 （姓氏） a surname Ⅱ動詞1 （加工處理生絲） treat soften and whiten s...
  • 結構 : 1 （各組成部分的搭配形式） structure; composition; construction; formation; constitution; fabric;...
  • 練習 : 1. （反復學習） practise; practice 2. （習題或作業等） exercise
  1. Firstly, second harmonic component ratio and dead angles of two phase inrush ' s dispersion in three - phase transformes are acted as input variable. secondly, the method applies improved algorithm based on the original algorithm of multi - layer forward back propagation network, that is to say, adding last variational effect of weight value and bias value to this time and making use of variable learning rate. at the same time, this method also adopts dynamic form in the number of hidden floor node

    首先，文中將三相變壓器兩相涌流差流的二次諧波含量比和間斷角作為網路的輸入變量；其次，利用對原有bp網路訓練演算法基礎上的改進型演算法（即在計算本次權值和閾值的變化時增加上一次權值和閾值變化的影響以及採用變學習率，與此同時隱含層神經元個數採用動態形式） ，通過樣本訓練使網路結構模型達到最優。
  2. This article puts forward a solution named divide - assemble by deducing the size of bp neural network to overcome entering the local best point, the dividing process is that a big bp neural network is divided into several small bp neural networks, every small bp neural network can study alone, after all small bp neural networks finish their study, we can assemble all these small bp neural networks into the quondam big bp neural networks ; on the basis of divide - assemble solution, this article discusses the preprocessing of input species and how to deduce the size of bp neural network further to make it easy to overcome entering the local best point ; for the study of every small bp neural network, this article adopts a solution named gdr - ga algorithm, which includes two algorithms. gdr ? a algorithm makes the merits of the two algorithms makeup each other to increase searching speed. finally, this article discusses the processing of atm band - width distribution dynamically

    本文從bp網的結構出發，以減小bp神經網路的規模為手段來克服陷入局部極小點，提出了bp神經網路的拆分組裝方法，即將一個大的bp網有機地拆分為幾個小的子bp網，每個子網的權值單獨訓練，訓練好以後，再將每個子網的單元和權值有機地組裝成原先的bp網，從理論和實驗上證明了該方法在解決局部極小值這一問題時是有效的；在拆分組裝方法基礎上，本文詳細闡述了輸入樣本的預處理過程，更進一步地減小了bp網路的規模，使子網的學習更加容易了；對于子網的學習，本文採用了最速梯度? ?遺傳混合演算法（即gdr ? ? ga演算法） ，使gdr演算法和ga演算法的優點互為補充，提高了收斂速度；最後本文闡述了用以上方法進行atm帶寬動態分配的過程。
  3. Frame is contoured for easy spotter access

    -框架式結構，令練習者更易使用。
  4. During hands - on exercises, you will work as part of a team to develop a project charter, a project scope statement, a work breakdown structure, and risk - based estimates for a real project provided by you or by another participant

    在動手練習中，你會作為一個團隊的一部分對一個由你或其他學員提供的真實個案進行實施其項目計劃，項目框架，工本結構及風險測算。
  5. To conquer this barrier, not only should the study on this structure be enhanced, the learners " conditions must be understood as well in order not to fly blind, so that we can " shoot the arrow right at the target " in textbook compiling, excise arranging and language teaching

    要解決這一難點，不僅要進一步加強對漢語述補結構的研究，而且還要對學習者的情況有所了解，這樣才能「知己知彼」 ，在教材的編寫、練習的編排及教學中做到「有的放矢」 。基於這一構想，我們做了這次調查。
  6. According to the modern education theory, we should adopt the following tactics in teaching the concept of chemistry : 1. use the vivid visual image to let the students gain the knowledge of the concept ; 2. create the atmosphere and let the students take part in the formation of the concept of chemistry ; 3. revise the old knowledge while learning the new one to realize the assimilation of concept ; 4. proceed step by step, lead the students deepen and develop the concept ; 5. give prominence to the understanding of the key words of the concept, get deeper understanding ; 6. pay attention to the relation between the concepts ; 7. optimize the study strategy and enhance the cognition standard, i. e. in the teaching of the concept of chemistry, we must pay great attention to the usage of various kinds of teaching method, including visual experiment, visual language and cai courseware, in order to help the students to understand the concept ; use the question to stimulate students " thoughts, give free rein to students " corpus, and let the students take part in the teaching process actively ; guide the students to remember new concepts and the help of their old knowledge ; pay attention to the levels of the concept, deepen and develop the concept continuously, use various ways to strengthen the meaning of the key words, help the students to master the concepts connotation, and give a clear extension, guide the students to found the concept system

    也就是說，在化學概念的教學中，要注意充分運用各種直觀教學手段，包括實驗直觀、語言直觀和cai課件直觀，幫助學生理解概念；注意運用問題啟動學生思維，發揮學生的主體性，使學生積極參與教學過程；要指導學生利用原有認知結構中適當的概念圖式來學習新概念；注意概念教學的層次性，不斷深化和發展概念；注意通過各種方式強化概念中關鍵字、詞的意義，幫助學生準確把握概念的內涵，清晰界定概念的外延；注意引導學生在應用中建立概念系統，形成合理的概念結構。同時在概念教學中還要注重學習方法的傳授和學習策略的形成，進行適當的元認知訓練，優化學生的學習策略，提高其元認知水平。根據化學概念的教學策略，化學概念的基本教學程序為:創設問題情境，引入概念;組織問題解決，建立概念;引導知識整理，概念系統化;指導練習應用，概念具體化。
  7. During hands - on exercises, you will work as part of a team to develop a project definition document, a work breakdown structure, range estimates, a network logic diagram, and a risk response analysis

    在實踐練習中，您將作為團隊的一員，參與設計項目的定義文件、工作分解結構、范圍估計、網路邏輯圖和風險應對分析。
  8. The reading and writing skills of learners are stressed, by providing moderate and proper language input, the learners can comprehend and master the language structure and practice what they have learned after fulfilling their reading tasks

    重點訓練學習者的讀寫能力，提供與學習者水平相適應、篇幅長短適度的語言輸入，讓學習者在完成閱讀任務的同時，理解和掌握語言結構，進而進行語言輸出實踐。
  9. A team or organization at this level follows defined methodological steps, uses process improvement techniques to enhance the methodological approach, conducts regular training programs, views the entire systems development process from an integration perspective, and utilizes more disciplined information engineering and structured development techniques

    處于這層的團隊和組織使用定義好的方法步驟，使用改進過程的技術來提高方法，管理有序的練習程序，從綜合的觀點看待整個系統開發過程，使用更加嚴格的信息工程和結構化開發技術。
  10. Neural networks are used more frequently in lossy data coding than in general lossless data coding, because standard neural networks must be trained off - line and they are too slow to be practical. in this thesis, statistical language model based on maximum entropy and neural networks are discussed particularly. then, an arithmetic coding algorithm based on maximum entropy and neural networks are proposed in this thesis

    傳統的人工神經網路數據編碼演算法需要離線訓練且編碼速度慢，因此通常多用於專用有損編碼領域如聲音、圖像編碼等，在無損數據編碼領域應用較少，針對這種現狀，本文詳細地研究了最大熵統計語言模型和神經網路演算法各自的特點，在此基礎上提出了一種基於神經網路和最大熵原理的算術編碼方法，這是一種自適應的可在線學習的演算法，並具有精簡的網路結構。
  11. Word training includes spelling, meaning and function. sentence training consists of comparative analysis and translation exercise, which are intended to help students avoid mistakes resulting from the negative influence of modes of chinese thinking

    句子的訓練方法有；針對學生寫作中易犯的錯誤通過對比分析和翻譯練習進行訓練，句式多多樣化訓練包括句子合併、句型轉換和平行結構訓練。
  12. Firstly, influence factors of generalization of neural network are presented in this thesis, in order to improve neural network ’ s generalization ability and dynamic knowledge acquirement adaptive ability, a structure auto - adaptive neural network new model based on genetic algorithm is proposed to optimize structure parameter of nn including hidden layer nodes, training epochs, initial weights, and so on ; secondly, through establishing integrating neural network and introducing data fusion technique, the integrality and precision of acquired knowledge is greatly improved. then aiming at the incompleteness and uncertainty problem consisting in the process of knowledge acquirement, knowledge acquirement method based on rough sets is explored to fulfill the rule extraction for intelligent diagnosis expert system, by completing missing value data and eliminating unnecessary attributes, discretization of continuous attribute, reducing redundancy, extracting rules in this thesis. finally, rough sets theory and neural network are combined to form rnn ( rough neural network ) model for acquiring knowledge, in which rough sets theory is employed to carry out some preprocessing and neural network is acted as one role of dynamic knowledge acquirement, and rnn can improve the speed and quality of knowledge acquirement greatly

    本文首先討論了影響神經網路的泛化能力的因素，提出了一種新的結構自適應神經網路學習演算法，在新方法中，採用了遺傳演算法對神經網路的結構參數（隱層節點數、訓練精度、初始權值）進行優化，大大提高了神經網路的泛化能力和知識動態獲取自適應能力;其次，構造集成神經網路，引入數據融合演算法，實現了基於集成神經網路的融合診斷，有效地提高了知識獲取的全面性、完善性及精度;然後，針對知識獲取過程中所存在的不確定性、不完備性等問題，探討了運用粗糙集理論的知識獲取方法，通過缺損數據補齊、連續數據的離散、沖突消除、冗餘信息約簡、知識規則抽取等一系列的演算法實現了智能診斷的知識規則獲取;最後，將粗糙集理論與神經網路相結合，研究了粗糙集-神經網路的知識獲取方法。
  13. All of these make the new teaching materials more agile and practicable. in content structure, above all, the two series of teaching materials are consistent with each other in knowledge structure, while the new ones increase and delete some knowledge points, which make the backbone stand out and the arrangement evident ; the structure of the new materials run reasonably through adjusting the sequence of certain paragraphs ; secondly, in capability structure, compared with the old ones, the new teaching material enhance the ability - trained degree, which acclimate the demand of eqo education ; thirdly, in deal - educated structure, the new teaching materials enlarge the connotation of idea quality training, instruct students in various ways, enhance the teaching materials educational value. in a word, the new teaching materials make a great progress in structure than the old ones

    從實質結構看，首先，在知識結構上，新教材與舊教材的知識體系大體保持一致，但是新教材通過增刪許多知識點，使教學內容主幹突出、層次分明，並更加貼近生活、聯系社會；通過調整某些章節的編排順序，使「三序」更好地結合起來；其次，在能力結構上，與舊教材相比，新教材分別在課文系統、實驗系統、練習系統中加大了能力培養的力度，順應了當前素質教育對學生提出的要求；最後，在思想品質上的培養方面，新教材擴展了思想品質培養的內涵，從多方面入手，採用多種方式教育學生，增強了教材的思想價值
  14. It solves these problems by using neural network based on fuzzy decison and neural network group. compared with traditional network, neural network based on fuzzy decison has simple structure, clear logic layer and short training time, while for network group, it is more intelligence and fuses uncertain information better without longer training time

    就此本文提出了基於模糊決策的神經網路和帶有加權融合的神經網路組兩種目標識別方法，與傳統的神經網路相比，基於模糊決策的神經網路結構簡單，邏輯層次分明，學習訓練演算法簡潔，而神經網路組在不增加訓練時間的基礎上，提高了網路的智能特性，能夠更加合理地對不確定性信息進行融合。
  15. Index of 6 one class : condition of construction of managerial guiding ideology, faculty, education and use, effect of education construction and reform, education management, education index of 15 2 class : school orientation and managerial train of thought, produce learn to grind ability of funds of condition of cathedral of infrastructure of structure of union, faculty, quality and construction, education, practice, education, professional, course, profession trains, quality is taught, training of competence of control of administrative team, quality, knowledge, obtain employment and social fame

    6項一級指標：辦學指導思想、師資隊伍建設、教學條件與利用、教學建設與改革、教學治理、教學效果15項二級指標：學校定位與辦學思路、產學研結合、師資隊伍結構、質量與建設、教學基礎設施、實踐教堂條件、教學經費、專業、課程、職業能力練習、素質教育、治理隊伍、質量控制、知識能力練習、就業與社會聲譽。
  16. It has many advantages, for example forces are transferred in a 3d manner, save steel material, large rigidity, small building highness, industrialized, beauty sculpt ect. there are tremendous buildings adopt space grid structure as roof domestic and abroad, including big gymnasium, airdrome, middle training house, exhibition hall, club, showplace, dinning hall and industry workshop

    它具有空間受力、節省鋼材、空間剛度大、建築高度小、便於工業化、定型化、建築造型美觀等優點，在國內外的人型體育館、停機場、中型練習館、展覽館、俱樂部、劇院、食堂以及工業廠房等工程的屋蓋結構中以得到廣泛應用。
  17. Realization of improved bp algorithm - single output three layers " artificial neural network generator base on improved bp algorithm has been developed by the author, and the generator has some functions that the number of neuron in first and second layer and theirs related training parameters such as learning rate. momentum factor a and the value of sum error e can all be self - defined by the users ; connection weights and threshold in each layer ' s neuron training data and teaching signals can also be input or modified in the friendly interface

    生成器功能是:網路結構中的第一、二層神經元個數和訓練參數中的學習速率粉，動量因子a和期望誤差值:可由用戶在一定范圍內自定義;各層的權值、閥值、網路初始樣本值及教師值可在友好的界面下輸入、修改。
  18. According to the requirements to pd pattern auto - recognition, this paper studies systematically the basic theories and realizable methods for auto - recognition of pd gray intensity image : ( 1 ) in the requirement of on - line pd monitoring for transformer, several discharge models are designed and the relevant experiment methods projected. with discharge model tests, a lot of discharge sample data is acquired. on the base of systematical research on recognition for pd gray intensity image, this paper puts forward two kinds of fractal features, the 2nd generalized dimensions of original pd images and fractal dimensions of high gray intensity pd images, and then the relevant extraction methods

    針對局部放電模式自動識別的需要，作者系統地研究了局部放電灰度圖像自動識別中的基本理論和實現方法： （ 1 ）根據變壓器局部放電在線監測的要求，設計了放電模型和實驗方法，並通過模型實驗獲得了大量放電樣本數據，為構造局部放電灰度圖像和採用bpnn進行識別作好準備； （ 2 ）研究了局部放電灰度圖像的構造方法以及降維構造32 32灰度和矩陣的方法；在用人工神經網路對局部放電進行模式識別時，分析了bp網路的優缺點，對典型bp網路的結構和學習訓練演算法提出了改進，採用帶有偏差單元的遞歸神經網路作為模式分類器；採用32 32灰度和矩陣進行bpnn識別結果表明這種方法是有效的。
  19. Campared with statistical analyze, it is shown that, the network structure and network output after trained rbfnn using improved rols is more reasonable than k - mean algrithm, and the control model has the property of self _ learning, self _ organization and self _ adaptive, and the control precision can be more than 90 %. on the other hand, this paper also shows that, rbfnn model can control the desulfuration process on the whole in time, and the prediction result using rbfnn model is better than statistical analyze method

    同統計分析結果比較，得出以下結論：利用改進rols演算法訓練rbf網路比k -均值演算法能夠得到更加合理的網路結構和網路輸出；利用rbfnn所建立的脫硫智能控制模型具有自學習性、自組織性和自適應性，其控制精度達到90 %以上； rbf神經網路模型基本可以對脫硫過程進行及時控制；基於rbfnn模型的預測效果優于傳統的統計分析結果。
  20. During the course of develop fault diagnostic method, the influence to the training circle number with network structure 、 learning rate 、 initial weight value & door value etc are discussed. by comprehensive analyses and comparing, the comparatively rational value is adopted to be network ' s eigenvalue

    在制粉系統故障診斷樣本訓練過程中，本文作者探討了網路結構、學習率、初始權值閾值等因素對訓練速度的影響，為選取合理的網路參數提供了依據。
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